To back up our goal of improving scientific research, we are offering these tips for claims analysis. When I say “claim analysis,” I mean examining of negative and positive consequences of your design feature. Think for a moment how does science work? Experiments, observations? Yes, that’s alright but above all scientists collect data and analyze it. As they say, “In God we trust, all others bring data.” Therefore when the scientific conclusions are made on actual data, the claims would be more reliable than claims made by anyone else. The criteria for determining these claims are given in this guide. Let’s have a look
Beware of Biased Research
Either intentional or unintentional, bias is bias. Generally, it’s the latter. If a study is designed below par, the outcomes can be skewed in one direction. There exist many different types of bias i.e., inclusive bias, procedural bias, and measurement bias. Among which the “omission bias is the most prevalent. It occurs when samples are selected for convenience. There is no issue with it as long as your research question cannot generalize the results to fit the entire population. However, enlisting students for studying first-line managers will not give a fully representative group.
Identify the Difference Between Not-Significant and no Effect
If your p-value comes out to be greater than 0.05, still it doesn’t indicate that “nothing happened.” At times data provide little or no evidence that the null hypothesis is false. So how can you claim non-significant results?Instead of claiming negative results which you can’t prove afterward, use a parameter “effect size.” It helps in working out the cause of the non-significant findings.
Bigger is Better in Case of Sample Size
You can more closely approximate the population by taking larger samples. The more substantial sample, the lesser would be uncertainty. It will help you claim the precision of your estimates. Not validity alone; in fact, the value of the standard error is directly dependent on the sample size.
At last, that final reason I can consider right now why bigger is better is that larger sample sizes give us more power. How big should my sample be? A good question to ask. Unfortunately, there is no sample answer to this question. It depends on the type of test, an individual choice. Complicating the matter further, different subjects contain a different notion of what constitutes a good sample size. It is not an easy matter. Therefore I’m suggesting a writer clinic for beginners buy using best dissertation writing services.
Identify Outliers in Your Data
An observation, unlike the other observations, falls outside the fence is classified as an outlier. So what? Should we remove the identified value? No. A good tip is to study whether there exists any pattern or systematic relationship to the outliers. If there is then perhaps that won’t be an outlier and can be explained. You can explore these outliers with visualizing tools such as box plot, scatter plot, and Z-score.
For any discipline, the measurement system needs to be evaluated very methodically and carefully. It covers survey questions, interview questions, operationalization, and conceptualization of your study constructs. As long as the reasoning behind the measure is fully explained, there should be no problem. Your research design and measurements will stand up to scrutiny. Therefore, choose your words carefully while mentioning your study measures i.e., “I have adopted these measures from X, Y, Z study.” Reviewers always look for these kinds of words. It gives them a signal that you were serious about your research process.
Throughout this guide, we saw what constitutes a good claim. I have mentioned techniques that can be used to analyze your study claims. However, a question about sample size left to be answered. I will return to that question again sometime. I hope this guide helped you in knowing study claims.